The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool.
The cortical thickness of the mammalian brain is an important morphological characteristic that can be used to investigate and observe the brain’s developmental changes that might be caused by biologically toxic substances such as ethanol or cocaine. Although various cortical thickness analysis methods have been proposed that are applicable for human brain and have developed into well-validated open-source software packages, cortical thickness analysis methods for rodent brains have not yet become as robust and accurate as those designed for human brains. Based on a previously proposed cortical thickness measurement pipeline for rodent brain analysis,1 we present an enhanced cortical thickness pipeline in terms of accuracy and anatomical consistency. First, we propose a Lagrangian-based computational approach in the thickness measurement step in order to minimize local truncation error using the fourth-order Runge-Kutta method. Second, by constructing a line object for each streamline of the thickness measurement, we can visualize the way the thickness is measured and achieve sub-voxel accuracy by performing geometric post-processing. Last, with emphasis on the importance of an anatomically consistent partial differential equation (PDE) boundary map, we propose an automatic PDE boundary map generation algorithm that is specific to rodent brain anatomy, which does not require manual labeling. The results show that the proposed cortical thickness pipeline can produce statistically significant regions that are not observed in the previous cortical thickness analysis pipeline.
We propose a novel multi-atlas segmentation method that employs a group-wise image registration method for the brain segmentation on rodent magnetic resonance (MR) images. The core element of the proposed segmentation is the use of a particle-guided image registration method that extends the concept of particle correspondence into the volumetric image domain. The registration method performs a group-wise image registration that simultaneously registers a set of images toward the space defined by the average of particles. The particle-guided image registration method is robust with low signal-to-noise ratio images as well as differing sizes and shapes observed in the developing rodent brain. Also, the use of an implicit common reference frame can prevent potential bias induced by the use of a single template in the segmentation process. We show that the use of a particle guided-image registration method can be naturally extended to a novel multi-atlas segmentation method and improves the registration method to explicitly use the provided template labels as an additional constraint. In the experiment, we show that our segmentation algorithm provides more accuracy with multi-atlas label fusion and stability against pair-wise image registration. The comparison with previous group-wise registration method is provided as well.
We present two new vortex-summarization techniques designed to portray vortex motion over an entire simulation and
over an ensemble of simulations in a single image. Linear “vortex core timelines” with cone glyphs summarize flow
over all time steps of a single simulation, with color varying to indicate time. Simplified “ribbon summarizations” with
hue nominally encoding ensemble membership and saturation encoding time enable direct visual comparison of the
distribution of vortices in time and space for a set of simulations.
Localized difference in the cortex is one of the most useful morphometric traits in human and
animal brain studies. There are many tools and methods already developed to automatically
measure and analyze cortical thickness for the human brain. However, these tools cannot be directly
applied to rodent brains due to the different scales; even adult rodent brains are 50 to 100
times smaller than humans. This paper describes an algorithm for automatically measuring the
cortical thickness of mouse and rat brains. The algorithm consists of three steps: segmentation,
thickness measurement, and statistical analysis among experimental groups. The segmentation
step provides the neocortex separation from other brain structures and thus is a preprocessing
step for the thickness measurement. In the thickness measurement step, the thickness is computed
by solving a Laplacian PDE and a transport equation. The Laplacian PDE first creates
streamlines as an analogy of cortical columns; the transport equation computes the length of
the streamlines. The result is stored as a thickness map over the neocortex surface. For the
statistical analysis, it is important to sample thickness at corresponding points. This is achieved
by the particle correspondence algorithm which minimizes entropy between dynamically moving
sample points called particles. Since the computational cost of the correspondence algorithm
may limit the number of corresponding points, we use thin-plate spline based interpolation to
increase the number of corresponding sample points. As a driving application, we measured the
thickness difference to assess the effects of adolescent intermittent ethanol exposure that persist
into adulthood and performed t-test between the control and exposed rat groups. We found
significantly differing regions in both hemispheres.
3D Magnetic Resonance (MR) and Diffusion Tensor Imaging (DTI) have become important
noninvasive tools for the study of animal models of brain development and neuropathologies.
Fully automated analysis methods adapted to rodent scale for these images will allow highthroughput
studies. A fundamental first step for most quantitative analysis algorithms is skullstripping,
which refers to the segmentation of the image into two tissue categories, brain and
non-brain. In this manuscript, we present a fully automatic skull-stripping algorithm in an atlasbased
manner. We also demonstrate how to either modify an external atlas or to build an atlas
from the population itself to present a self-contained approach. We applied our method to three
datasets of rat brain scans, at different ages (PND5, PND14 and adult), different study groups
(control, ethanol exposed, intrauterine cocaine exposed), as well as different image acquisition
parameters. We validated our method by comparing the automated skull-strip results to manual
delineations performed by our expert, which showed a discrepancy of less than a single voxel
on average. We thus demonstrate that our algorithm can robustly and accurately perform the
skull-stripping within one voxel of the manual delineation, and in a fraction of the time it takes
a human expert.
Magentic Reasonance Imaging for mouse phenotype study is one of the important tools to understand human
diseases. In this paper, we present a fully automatic pipeline for the process of morphometric mouse brain
analysis. The method is based on atlas-based tissue and regional segmentation, which was originally developed
for the human brain. To evaluate our method, we conduct a qualitative and quantitative validation study as
well as compare of b-spline and fluid registration methods as components in the pipeline. The validation study
includes visual inspection, shape and volumetric measurements and stability of the registration methods against
various parameter settings in the processing pipeline. The result shows both fluid and b-spline registration
methods work well in murine settings, but the fluid registration is more stable. Additionally, we evaluated our
segmentation methods by comparing volume differences between Fmr1 FXS in FVB background vs C57BL/6J
mouse strains.
In this paper, we present MIDAS, a web-based digital archiving system that processes large collections of data. Medical imaging research often involves interdisciplinary teams, each performing a separate task, from acquiring datasets to analyzing the processing results. Moreover, the number and size of the datasets continue to increase every year due to recent advancements in acquisition technology. As a result, many research laboratories centralize their data and rely on distributed computing power. We created a web-based digital archiving repository based on openstandards. The MIDAS repository is specifically tuned for medical and scientific datasets and provides a flexible data management facility, a search engine, and an online image viewer. MIDAS enables users to run a set of extensible image processing algorithms from the web to the selected datasets and to add new algorithms to the MIDAS system, facilitating the dissemination of users' work to different research partners. The MIDAS system is currently running in several research laboratories and has demonstrated its ability to streamline the full image processing workflow from data acquisition to image analysis and reports.
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